Examples with PrimitiveTypeInfo org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo used on opensource projects

Example 41 with PrimitiveTypeInfo

use of org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo in project hive by apache.

the class DDLSemanticAnalyzer method getFullPartitionSpecs.

/**
   * Get the partition specs from the tree. This stores the full specification
   * with the comparator operator into the output list.
   *
   * @param ast Tree to extract partitions from.
   * @param tab Table.
   * @return    Map of partitions by prefix length. Most of the time prefix length will
   *            be the same for all partition specs, so we can just OR the expressions.
   */
private Map<Integer, List<ExprNodeGenericFuncDesc>> getFullPartitionSpecs(CommonTree ast, Table tab, boolean canGroupExprs) throws SemanticException {
    String defaultPartitionName = HiveConf.getVar(conf, HiveConf.ConfVars.DEFAULTPARTITIONNAME);
    Map<String, String> colTypes = new HashMap<String, String>();
    for (FieldSchema fs : tab.getPartitionKeys()) {
        colTypes.put(fs.getName().toLowerCase(), fs.getType());
    }
    Map<Integer, List<ExprNodeGenericFuncDesc>> result = new HashMap<Integer, List<ExprNodeGenericFuncDesc>>();
    for (int childIndex = 0; childIndex < ast.getChildCount(); childIndex++) {
        Tree partSpecTree = ast.getChild(childIndex);
        if (partSpecTree.getType() != HiveParser.TOK_PARTSPEC)
            continue;
        ExprNodeGenericFuncDesc expr = null;
        HashSet<String> names = new HashSet<String>(partSpecTree.getChildCount());
        for (int i = 0; i < partSpecTree.getChildCount(); ++i) {
            CommonTree partSpecSingleKey = (CommonTree) partSpecTree.getChild(i);
            assert (partSpecSingleKey.getType() == HiveParser.TOK_PARTVAL);
            String key = stripIdentifierQuotes(partSpecSingleKey.getChild(0).getText()).toLowerCase();
            String operator = partSpecSingleKey.getChild(1).getText();
            ASTNode partValNode = (ASTNode) partSpecSingleKey.getChild(2);
            TypeCheckCtx typeCheckCtx = new TypeCheckCtx(null);
            ExprNodeConstantDesc valExpr = (ExprNodeConstantDesc) TypeCheckProcFactory.genExprNode(partValNode, typeCheckCtx).get(partValNode);
            Object val = valExpr.getValue();
            boolean isDefaultPartitionName = val.equals(defaultPartitionName);
            String type = colTypes.get(key);
            PrimitiveTypeInfo pti = TypeInfoFactory.getPrimitiveTypeInfo(type);
            if (type == null) {
                throw new SemanticException("Column " + key + " not found");
            }
            // Create the corresponding hive expression to filter on partition columns.
            if (!isDefaultPartitionName) {
                if (!valExpr.getTypeString().equals(type)) {
                    Converter converter = ObjectInspectorConverters.getConverter(TypeInfoUtils.getStandardJavaObjectInspectorFromTypeInfo(valExpr.getTypeInfo()), TypeInfoUtils.getStandardJavaObjectInspectorFromTypeInfo(pti));
                    val = converter.convert(valExpr.getValue());
                }
            }
            ExprNodeColumnDesc column = new ExprNodeColumnDesc(pti, key, null, true);
            ExprNodeGenericFuncDesc op = makeBinaryPredicate(operator, column, isDefaultPartitionName ? new ExprNodeConstantDefaultDesc(pti, defaultPartitionName) : new ExprNodeConstantDesc(pti, val));
            // If it's multi-expr filter (e.g. a='5', b='2012-01-02'), AND with previous exprs.
            expr = (expr == null) ? op : makeBinaryPredicate("and", expr, op);
            names.add(key);
        }
        if (expr == null)
            continue;
        // We got the expr for one full partition spec. Determine the prefix length.
        int prefixLength = calculatePartPrefix(tab, names);
        List<ExprNodeGenericFuncDesc> orExpr = result.get(prefixLength);
        // If we don't, create a new separate filter. In most cases there will only be one.
        if (orExpr == null) {
            result.put(prefixLength, Lists.newArrayList(expr));
        } else if (canGroupExprs) {
            orExpr.set(0, makeBinaryPredicate("or", expr, orExpr.get(0)));
        } else {
            orExpr.add(expr);
        }
    }
    return result;
}

Example 42 with PrimitiveTypeInfo

use of org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo in project hive by apache.

the class HBaseStore method getPartitionNamesPrunedByExprNoTxn.

/**
   * Gets the partition names from a table, pruned using an expression.
   * @param table Table.
   * @param expr Expression.
   * @param defaultPartName Default partition name from job config, if any.
   * @param maxParts Maximum number of partition names to return.
   * @param result The resulting names.
   * @return Whether the result contains any unknown partitions.
   * @throws NoSuchObjectException
   */
private boolean getPartitionNamesPrunedByExprNoTxn(Table table, byte[] expr, String defaultPartName, short maxParts, List<String> result) throws MetaException, NoSuchObjectException {
    List<Partition> parts = getPartitions(table.getDbName(), table.getTableName(), maxParts);
    for (Partition part : parts) {
        result.add(Warehouse.makePartName(table.getPartitionKeys(), part.getValues()));
    }
    List<String> columnNames = new ArrayList<String>();
    List<PrimitiveTypeInfo> typeInfos = new ArrayList<PrimitiveTypeInfo>();
    for (FieldSchema fs : table.getPartitionKeys()) {
        columnNames.add(fs.getName());
        typeInfos.add(TypeInfoFactory.getPrimitiveTypeInfo(fs.getType()));
    }
    if (defaultPartName == null || defaultPartName.isEmpty()) {
        defaultPartName = HiveConf.getVar(getConf(), HiveConf.ConfVars.DEFAULTPARTITIONNAME);
    }
    return expressionProxy.filterPartitionsByExpr(columnNames, typeInfos, expr, defaultPartName, result);
}

Example 43 with PrimitiveTypeInfo

use of org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo in project hive by apache.

the class ConstantPropagateProcFactory method evaluateFunction.

/**
   * Evaluate UDF
   *
   * @param udf UDF object
   * @param exprs
   * @param oldExprs
   * @return null if expression cannot be evaluated (not all parameters are constants). Or evaluated
   *         ExprNodeConstantDesc if possible.
   * @throws HiveException
   */
private static ExprNodeDesc evaluateFunction(GenericUDF udf, List<ExprNodeDesc> exprs, List<ExprNodeDesc> oldExprs) {
    DeferredJavaObject[] arguments = new DeferredJavaObject[exprs.size()];
    ObjectInspector[] argois = new ObjectInspector[exprs.size()];
    for (int i = 0; i < exprs.size(); i++) {
        ExprNodeDesc desc = exprs.get(i);
        if (desc instanceof ExprNodeConstantDesc) {
            ExprNodeConstantDesc constant = (ExprNodeConstantDesc) exprs.get(i);
            if (!constant.getTypeInfo().equals(oldExprs.get(i).getTypeInfo())) {
                constant = typeCast(constant, oldExprs.get(i).getTypeInfo());
                if (constant == null) {
                    return null;
                }
            }
            if (constant.getTypeInfo().getCategory() != Category.PRIMITIVE) {
                // nested complex types cannot be folded cleanly
                return null;
            }
            Object value = constant.getValue();
            PrimitiveTypeInfo pti = (PrimitiveTypeInfo) constant.getTypeInfo();
            Object writableValue = null == value ? value : PrimitiveObjectInspectorFactory.getPrimitiveJavaObjectInspector(pti).getPrimitiveWritableObject(value);
            arguments[i] = new DeferredJavaObject(writableValue);
            argois[i] = ObjectInspectorUtils.getConstantObjectInspector(constant.getWritableObjectInspector(), writableValue);
        } else if (desc instanceof ExprNodeGenericFuncDesc) {
            ExprNodeDesc evaluatedFn = foldExpr((ExprNodeGenericFuncDesc) desc);
            if (null == evaluatedFn || !(evaluatedFn instanceof ExprNodeConstantDesc)) {
                return null;
            }
            ExprNodeConstantDesc constant = (ExprNodeConstantDesc) evaluatedFn;
            if (constant.getTypeInfo().getCategory() != Category.PRIMITIVE) {
                // nested complex types cannot be folded cleanly
                return null;
            }
            Object writableValue = PrimitiveObjectInspectorFactory.getPrimitiveJavaObjectInspector((PrimitiveTypeInfo) constant.getTypeInfo()).getPrimitiveWritableObject(constant.getValue());
            arguments[i] = new DeferredJavaObject(writableValue);
            argois[i] = ObjectInspectorUtils.getConstantObjectInspector(constant.getWritableObjectInspector(), writableValue);
        } else {
            return null;
        }
    }
    try {
        ObjectInspector oi = udf.initialize(argois);
        Object o = udf.evaluate(arguments);
        if (LOG.isDebugEnabled()) {
            LOG.debug(udf.getClass().getName() + "(" + exprs + ")=" + o);
        }
        if (o == null) {
            return new ExprNodeConstantDesc(TypeInfoUtils.getTypeInfoFromObjectInspector(oi), o);
        }
        Class<?> clz = o.getClass();
        if (PrimitiveObjectInspectorUtils.isPrimitiveWritableClass(clz)) {
            PrimitiveObjectInspector poi = (PrimitiveObjectInspector) oi;
            TypeInfo typeInfo = poi.getTypeInfo();
            o = poi.getPrimitiveJavaObject(o);
            if (typeInfo.getTypeName().contains(serdeConstants.DECIMAL_TYPE_NAME) || typeInfo.getTypeName().contains(serdeConstants.VARCHAR_TYPE_NAME) || typeInfo.getTypeName().contains(serdeConstants.CHAR_TYPE_NAME)) {
                return new ExprNodeConstantDesc(typeInfo, o);
            }
        } else if (udf instanceof GenericUDFStruct && oi instanceof StandardConstantStructObjectInspector) {
            // do not fold named_struct, only struct()
            ConstantObjectInspector coi = (ConstantObjectInspector) oi;
            TypeInfo structType = TypeInfoUtils.getTypeInfoFromObjectInspector(coi);
            return new ExprNodeConstantDesc(structType, ObjectInspectorUtils.copyToStandardJavaObject(o, coi));
        } else if (!PrimitiveObjectInspectorUtils.isPrimitiveJavaClass(clz)) {
            if (LOG.isErrorEnabled()) {
                LOG.error("Unable to evaluate " + udf + ". Return value unrecoginizable.");
            }
            return null;
        } else {
        // fall through
        }
        String constStr = null;
        if (arguments.length == 1 && FunctionRegistry.isOpCast(udf)) {
            // remember original string representation of constant.
            constStr = arguments[0].get().toString();
        }
        return new ExprNodeConstantDesc(o).setFoldedFromVal(constStr);
    } catch (HiveException e) {
        LOG.error("Evaluation function " + udf.getClass() + " failed in Constant Propagation Optimizer.");
        throw new RuntimeException(e);
    }
}

Example 44 with PrimitiveTypeInfo

use of org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo in project hive by apache.

the class Vectorizer method canSpecializeMapJoin.

private boolean canSpecializeMapJoin(Operator<? extends OperatorDesc> op, MapJoinDesc desc, boolean isTezOrSpark, VectorizationContext vContext, VectorMapJoinInfo vectorMapJoinInfo) throws HiveException {
    Preconditions.checkState(op instanceof MapJoinOperator);
    // Allocate a VectorReduceSinkDesc initially with implementation type NONE so EXPLAIN
    // can report this operator was vectorized, but not native.  And, the conditions.
    VectorMapJoinDesc vectorDesc = new VectorMapJoinDesc();
    desc.setVectorDesc(vectorDesc);
    boolean isVectorizationMapJoinNativeEnabled = HiveConf.getBoolVar(hiveConf, HiveConf.ConfVars.HIVE_VECTORIZATION_MAPJOIN_NATIVE_ENABLED);
    String engine = HiveConf.getVar(hiveConf, HiveConf.ConfVars.HIVE_EXECUTION_ENGINE);
    boolean oneMapJoinCondition = (desc.getConds().length == 1);
    boolean hasNullSafes = onExpressionHasNullSafes(desc);
    byte posBigTable = (byte) desc.getPosBigTable();
    // Since we want to display all the met and not met conditions in EXPLAIN, we determine all
    // information first....
    List<ExprNodeDesc> keyDesc = desc.getKeys().get(posBigTable);
    VectorExpression[] allBigTableKeyExpressions = vContext.getVectorExpressions(keyDesc);
    final int allBigTableKeyExpressionsLength = allBigTableKeyExpressions.length;
    // Assume.
    boolean supportsKeyTypes = true;
    HashSet<String> notSupportedKeyTypes = new HashSet<String>();
    // Since a key expression can be a calculation and the key will go into a scratch column,
    // we need the mapping and type information.
    int[] bigTableKeyColumnMap = new int[allBigTableKeyExpressionsLength];
    String[] bigTableKeyColumnNames = new String[allBigTableKeyExpressionsLength];
    TypeInfo[] bigTableKeyTypeInfos = new TypeInfo[allBigTableKeyExpressionsLength];
    ArrayList<VectorExpression> bigTableKeyExpressionsList = new ArrayList<VectorExpression>();
    VectorExpression[] bigTableKeyExpressions;
    for (int i = 0; i < allBigTableKeyExpressionsLength; i++) {
        VectorExpression ve = allBigTableKeyExpressions[i];
        if (!IdentityExpression.isColumnOnly(ve)) {
            bigTableKeyExpressionsList.add(ve);
        }
        bigTableKeyColumnMap[i] = ve.getOutputColumn();
        ExprNodeDesc exprNode = keyDesc.get(i);
        bigTableKeyColumnNames[i] = exprNode.toString();
        TypeInfo typeInfo = exprNode.getTypeInfo();
        // same check used in HashTableLoader.
        if (!MapJoinKey.isSupportedField(typeInfo)) {
            supportsKeyTypes = false;
            Category category = typeInfo.getCategory();
            notSupportedKeyTypes.add((category != Category.PRIMITIVE ? category.toString() : ((PrimitiveTypeInfo) typeInfo).getPrimitiveCategory().toString()));
        }
        bigTableKeyTypeInfos[i] = typeInfo;
    }
    if (bigTableKeyExpressionsList.size() == 0) {
        bigTableKeyExpressions = null;
    } else {
        bigTableKeyExpressions = bigTableKeyExpressionsList.toArray(new VectorExpression[0]);
    }
    List<ExprNodeDesc> bigTableExprs = desc.getExprs().get(posBigTable);
    VectorExpression[] allBigTableValueExpressions = vContext.getVectorExpressions(bigTableExprs);
    boolean isFastHashTableEnabled = HiveConf.getBoolVar(hiveConf, HiveConf.ConfVars.HIVE_VECTORIZATION_MAPJOIN_NATIVE_FAST_HASHTABLE_ENABLED);
    // Especially since LLAP is prone to turn it off in the MapJoinDesc in later
    // physical optimizer stages...
    boolean isHybridHashJoin = desc.isHybridHashJoin();
    /*
     * Populate vectorMapJoininfo.
     */
    /*
     * Similarly, we need a mapping since a value expression can be a calculation and the value
     * will go into a scratch column.
     */
    int[] bigTableValueColumnMap = new int[allBigTableValueExpressions.length];
    String[] bigTableValueColumnNames = new String[allBigTableValueExpressions.length];
    TypeInfo[] bigTableValueTypeInfos = new TypeInfo[allBigTableValueExpressions.length];
    ArrayList<VectorExpression> bigTableValueExpressionsList = new ArrayList<VectorExpression>();
    VectorExpression[] bigTableValueExpressions;
    for (int i = 0; i < bigTableValueColumnMap.length; i++) {
        VectorExpression ve = allBigTableValueExpressions[i];
        if (!IdentityExpression.isColumnOnly(ve)) {
            bigTableValueExpressionsList.add(ve);
        }
        bigTableValueColumnMap[i] = ve.getOutputColumn();
        ExprNodeDesc exprNode = bigTableExprs.get(i);
        bigTableValueColumnNames[i] = exprNode.toString();
        bigTableValueTypeInfos[i] = exprNode.getTypeInfo();
    }
    if (bigTableValueExpressionsList.size() == 0) {
        bigTableValueExpressions = null;
    } else {
        bigTableValueExpressions = bigTableValueExpressionsList.toArray(new VectorExpression[0]);
    }
    vectorMapJoinInfo.setBigTableKeyColumnMap(bigTableKeyColumnMap);
    vectorMapJoinInfo.setBigTableKeyColumnNames(bigTableKeyColumnNames);
    vectorMapJoinInfo.setBigTableKeyTypeInfos(bigTableKeyTypeInfos);
    vectorMapJoinInfo.setBigTableKeyExpressions(bigTableKeyExpressions);
    vectorMapJoinInfo.setBigTableValueColumnMap(bigTableValueColumnMap);
    vectorMapJoinInfo.setBigTableValueColumnNames(bigTableValueColumnNames);
    vectorMapJoinInfo.setBigTableValueTypeInfos(bigTableValueTypeInfos);
    vectorMapJoinInfo.setBigTableValueExpressions(bigTableValueExpressions);
    /*
     * Small table information.
     */
    VectorColumnOutputMapping bigTableRetainedMapping = new VectorColumnOutputMapping("Big Table Retained Mapping");
    VectorColumnOutputMapping bigTableOuterKeyMapping = new VectorColumnOutputMapping("Big Table Outer Key Mapping");
    // The order of the fields in the LazyBinary small table value must be used, so
    // we use the source ordering flavor for the mapping.
    VectorColumnSourceMapping smallTableMapping = new VectorColumnSourceMapping("Small Table Mapping");
    Byte[] order = desc.getTagOrder();
    Byte posSingleVectorMapJoinSmallTable = (order[0] == posBigTable ? order[1] : order[0]);
    boolean isOuterJoin = !desc.getNoOuterJoin();
    /*
     * Gather up big and small table output result information from the MapJoinDesc.
     */
    List<Integer> bigTableRetainList = desc.getRetainList().get(posBigTable);
    int bigTableRetainSize = bigTableRetainList.size();
    int[] smallTableIndices;
    int smallTableIndicesSize;
    List<ExprNodeDesc> smallTableExprs = desc.getExprs().get(posSingleVectorMapJoinSmallTable);
    if (desc.getValueIndices() != null && desc.getValueIndices().get(posSingleVectorMapJoinSmallTable) != null) {
        smallTableIndices = desc.getValueIndices().get(posSingleVectorMapJoinSmallTable);
        smallTableIndicesSize = smallTableIndices.length;
    } else {
        smallTableIndices = null;
        smallTableIndicesSize = 0;
    }
    List<Integer> smallTableRetainList = desc.getRetainList().get(posSingleVectorMapJoinSmallTable);
    int smallTableRetainSize = smallTableRetainList.size();
    int smallTableResultSize = 0;
    if (smallTableIndicesSize > 0) {
        smallTableResultSize = smallTableIndicesSize;
    } else if (smallTableRetainSize > 0) {
        smallTableResultSize = smallTableRetainSize;
    }
    /*
     * Determine the big table retained mapping first so we can optimize out (with
     * projection) copying inner join big table keys in the subsequent small table results section.
     */
    // We use a mapping object here so we can build the projection in any order and
    // get the ordered by 0 to n-1 output columns at the end.
    //
    // Also, to avoid copying a big table key into the small table result area for inner joins,
    // we reference it with the projection so there can be duplicate output columns
    // in the projection.
    VectorColumnSourceMapping projectionMapping = new VectorColumnSourceMapping("Projection Mapping");
    int nextOutputColumn = (order[0] == posBigTable ? 0 : smallTableResultSize);
    for (int i = 0; i < bigTableRetainSize; i++) {
        // Since bigTableValueExpressions may do a calculation and produce a scratch column, we
        // need to map to the right batch column.
        int retainColumn = bigTableRetainList.get(i);
        int batchColumnIndex = bigTableValueColumnMap[retainColumn];
        TypeInfo typeInfo = bigTableValueTypeInfos[i];
        // With this map we project the big table batch to make it look like an output batch.
        projectionMapping.add(nextOutputColumn, batchColumnIndex, typeInfo);
        // Collect columns we copy from the big table batch to the overflow batch.
        if (!bigTableRetainedMapping.containsOutputColumn(batchColumnIndex)) {
            // Tolerate repeated use of a big table column.
            bigTableRetainedMapping.add(batchColumnIndex, batchColumnIndex, typeInfo);
        }
        nextOutputColumn++;
    }
    /*
     * Now determine the small table results.
     */
    boolean smallTableExprVectorizes = true;
    int firstSmallTableOutputColumn;
    firstSmallTableOutputColumn = (order[0] == posBigTable ? bigTableRetainSize : 0);
    int smallTableOutputCount = 0;
    nextOutputColumn = firstSmallTableOutputColumn;
    // Small table indices has more information (i.e. keys) than retain, so use it if it exists...
    String[] bigTableRetainedNames;
    if (smallTableIndicesSize > 0) {
        smallTableOutputCount = smallTableIndicesSize;
        bigTableRetainedNames = new String[smallTableOutputCount];
        for (int i = 0; i < smallTableIndicesSize; i++) {
            if (smallTableIndices[i] >= 0) {
                // Zero and above numbers indicate a big table key is needed for
                // small table result "area".
                int keyIndex = smallTableIndices[i];
                // Since bigTableKeyExpressions may do a calculation and produce a scratch column, we
                // need to map the right column.
                int batchKeyColumn = bigTableKeyColumnMap[keyIndex];
                bigTableRetainedNames[i] = bigTableKeyColumnNames[keyIndex];
                TypeInfo typeInfo = bigTableKeyTypeInfos[keyIndex];
                if (!isOuterJoin) {
                    // Optimize inner join keys of small table results.
                    // Project the big table key into the small table result "area".
                    projectionMapping.add(nextOutputColumn, batchKeyColumn, typeInfo);
                    if (!bigTableRetainedMapping.containsOutputColumn(batchKeyColumn)) {
                        // If necessary, copy the big table key into the overflow batch's small table
                        // result "area".
                        bigTableRetainedMapping.add(batchKeyColumn, batchKeyColumn, typeInfo);
                    }
                } else {
                    // For outer joins, since the small table key can be null when there is no match,
                    // we must have a physical (scratch) column for those keys.  We cannot use the
                    // projection optimization used by inner joins above.
                    int scratchColumn = vContext.allocateScratchColumn(typeInfo);
                    projectionMapping.add(nextOutputColumn, scratchColumn, typeInfo);
                    bigTableRetainedMapping.add(batchKeyColumn, scratchColumn, typeInfo);
                    bigTableOuterKeyMapping.add(batchKeyColumn, scratchColumn, typeInfo);
                }
            } else {
                // Negative numbers indicate a column to be (deserialize) read from the small table's
                // LazyBinary value row.
                int smallTableValueIndex = -smallTableIndices[i] - 1;
                ExprNodeDesc smallTableExprNode = smallTableExprs.get(i);
                if (!validateExprNodeDesc(smallTableExprNode, "Small Table")) {
                    clearNotVectorizedReason();
                    smallTableExprVectorizes = false;
                }
                bigTableRetainedNames[i] = smallTableExprNode.toString();
                TypeInfo typeInfo = smallTableExprNode.getTypeInfo();
                // Make a new big table scratch column for the small table value.
                int scratchColumn = vContext.allocateScratchColumn(typeInfo);
                projectionMapping.add(nextOutputColumn, scratchColumn, typeInfo);
                smallTableMapping.add(smallTableValueIndex, scratchColumn, typeInfo);
            }
            nextOutputColumn++;
        }
    } else if (smallTableRetainSize > 0) {
        smallTableOutputCount = smallTableRetainSize;
        bigTableRetainedNames = new String[smallTableOutputCount];
        for (int i = 0; i < smallTableRetainSize; i++) {
            int smallTableValueIndex = smallTableRetainList.get(i);
            ExprNodeDesc smallTableExprNode = smallTableExprs.get(i);
            if (!validateExprNodeDesc(smallTableExprNode, "Small Table")) {
                clearNotVectorizedReason();
                smallTableExprVectorizes = false;
            }
            bigTableRetainedNames[i] = smallTableExprNode.toString();
            // Make a new big table scratch column for the small table value.
            TypeInfo typeInfo = smallTableExprNode.getTypeInfo();
            int scratchColumn = vContext.allocateScratchColumn(typeInfo);
            projectionMapping.add(nextOutputColumn, scratchColumn, typeInfo);
            smallTableMapping.add(smallTableValueIndex, scratchColumn, typeInfo);
            nextOutputColumn++;
        }
    } else {
        bigTableRetainedNames = new String[0];
    }
    boolean useOptimizedTable = HiveConf.getBoolVar(hiveConf, HiveConf.ConfVars.HIVEMAPJOINUSEOPTIMIZEDTABLE);
    // Remember the condition variables for EXPLAIN regardless of whether we specialize or not.
    vectorDesc.setUseOptimizedTable(useOptimizedTable);
    vectorDesc.setIsVectorizationMapJoinNativeEnabled(isVectorizationMapJoinNativeEnabled);
    vectorDesc.setEngine(engine);
    vectorDesc.setOneMapJoinCondition(oneMapJoinCondition);
    vectorDesc.setHasNullSafes(hasNullSafes);
    vectorDesc.setSmallTableExprVectorizes(smallTableExprVectorizes);
    vectorDesc.setIsFastHashTableEnabled(isFastHashTableEnabled);
    vectorDesc.setIsHybridHashJoin(isHybridHashJoin);
    vectorDesc.setSupportsKeyTypes(supportsKeyTypes);
    if (!supportsKeyTypes) {
        vectorDesc.setNotSupportedKeyTypes(new ArrayList(notSupportedKeyTypes));
    }
    // Check common conditions for both Optimized and Fast Hash Tables.
    // Assume.
    boolean result = true;
    if (!useOptimizedTable || !isVectorizationMapJoinNativeEnabled || !isTezOrSpark || !oneMapJoinCondition || hasNullSafes || !smallTableExprVectorizes) {
        result = false;
    }
    if (!isFastHashTableEnabled) {
        // Check optimized-only hash table restrictions.
        if (!supportsKeyTypes) {
            result = false;
        }
    } else {
        if (isHybridHashJoin) {
            result = false;
        }
    }
    // Convert dynamic arrays and maps to simple arrays.
    bigTableRetainedMapping.finalize();
    bigTableOuterKeyMapping.finalize();
    smallTableMapping.finalize();
    vectorMapJoinInfo.setBigTableRetainedMapping(bigTableRetainedMapping);
    vectorMapJoinInfo.setBigTableOuterKeyMapping(bigTableOuterKeyMapping);
    vectorMapJoinInfo.setSmallTableMapping(smallTableMapping);
    projectionMapping.finalize();
    // Verify we added an entry for each output.
    assert projectionMapping.isSourceSequenceGood();
    vectorMapJoinInfo.setProjectionMapping(projectionMapping);
    return result;
}

Example 45 with PrimitiveTypeInfo

use of org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo in project hive by apache.

the class Vectorizer method specializeReduceSinkOperator.

private Operator<? extends OperatorDesc> specializeReduceSinkOperator(Operator<? extends OperatorDesc> op, VectorizationContext vContext, ReduceSinkDesc desc, VectorReduceSinkInfo vectorReduceSinkInfo) throws HiveException {
    Operator<? extends OperatorDesc> vectorOp = null;
    Class<? extends Operator<?>> opClass = null;
    Type[] reduceSinkKeyColumnVectorTypes = vectorReduceSinkInfo.getReduceSinkKeyColumnVectorTypes();
    // By default, we can always use the multi-key class.
    VectorReduceSinkDesc.ReduceSinkKeyType reduceSinkKeyType = VectorReduceSinkDesc.ReduceSinkKeyType.MULTI_KEY;
    // Look for single column optimization.
    if (reduceSinkKeyColumnVectorTypes.length == 1) {
        LOG.info("Vectorizer vectorizeOperator groupby typeName " + vectorReduceSinkInfo.getReduceSinkKeyTypeInfos()[0]);
        Type columnVectorType = reduceSinkKeyColumnVectorTypes[0];
        switch(columnVectorType) {
            case LONG:
                {
                    PrimitiveCategory primitiveCategory = ((PrimitiveTypeInfo) vectorReduceSinkInfo.getReduceSinkKeyTypeInfos()[0]).getPrimitiveCategory();
                    switch(primitiveCategory) {
                        case BOOLEAN:
                        case BYTE:
                        case SHORT:
                        case INT:
                        case LONG:
                            reduceSinkKeyType = VectorReduceSinkDesc.ReduceSinkKeyType.LONG;
                            break;
                        default:
                            // Other integer types not supported yet.
                            break;
                    }
                }
                break;
            case BYTES:
                reduceSinkKeyType = VectorReduceSinkDesc.ReduceSinkKeyType.STRING;
            default:
                // Stay with multi-key.
                break;
        }
    }
    switch(reduceSinkKeyType) {
        case LONG:
            opClass = VectorReduceSinkLongOperator.class;
            break;
        case STRING:
            opClass = VectorReduceSinkStringOperator.class;
            break;
        case MULTI_KEY:
            opClass = VectorReduceSinkMultiKeyOperator.class;
            break;
        default:
            throw new HiveException("Unknown reduce sink key type " + reduceSinkKeyType);
    }
    VectorReduceSinkDesc vectorDesc = (VectorReduceSinkDesc) desc.getVectorDesc();
    vectorDesc.setReduceSinkKeyType(reduceSinkKeyType);
    vectorDesc.setVectorReduceSinkInfo(vectorReduceSinkInfo);
    vectorOp = OperatorFactory.getVectorOperator(opClass, op.getCompilationOpContext(), op.getConf(), vContext);
    LOG.info("Vectorizer vectorizeOperator reduce sink class " + vectorOp.getClass().getSimpleName());
    return vectorOp;
}